U.S. patent application number 16/757066 was filed with the patent office on 2020-10-29 for physiological signal processing apparatus and method.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Warner Rudolph Theophile TEN KATE, Giulio VALENTI.
Application Number | 20200337570 16/757066 |
Document ID | / |
Family ID | 1000004972718 |
Filed Date | 2020-10-29 |
![](/patent/app/20200337570/US20200337570A1-20201029-D00000.png)
![](/patent/app/20200337570/US20200337570A1-20201029-D00001.png)
![](/patent/app/20200337570/US20200337570A1-20201029-D00002.png)
United States Patent
Application |
20200337570 |
Kind Code |
A1 |
VALENTI; Giulio ; et
al. |
October 29, 2020 |
PHYSIOLOGICAL SIGNAL PROCESSING APPARATUS AND METHOD
Abstract
A method and system for analyzing two related physiological
signals to determine a time difference between them. The
fundamental frequency components are analyzed to determine a time
difference. This approach is robust against varying and different
wave 5 shapes, providing a stable and accurate determination of a
time difference.
Inventors: |
VALENTI; Giulio; (Eindhoven,
NL) ; TEN KATE; Warner Rudolph Theophile; (Waalre,
NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
|
NL |
|
|
Family ID: |
1000004972718 |
Appl. No.: |
16/757066 |
Filed: |
October 23, 2018 |
PCT Filed: |
October 23, 2018 |
PCT NO: |
PCT/EP2018/079045 |
371 Date: |
April 17, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7246 20130101;
A61B 2562/0247 20130101; A61B 5/0402 20130101; A61B 5/7257
20130101; A61B 2562/0219 20130101; A61B 5/02125 20130101 |
International
Class: |
A61B 5/021 20060101
A61B005/021; A61B 5/00 20060101 A61B005/00; A61B 5/0402 20060101
A61B005/0402 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 26, 2017 |
EP |
17198539.3 |
Claims
1. A computer-implemented method of analyzing two related
physiological signals to determine a time difference between them,
comprising: receiving a first periodic physiological signal;
receiving a second periodic physiological signal induced by the
same physiological process as the first periodic physiological
signal; identifying a fundamental frequency component of the first
and second signals; and determining a time difference between the
fundamental frequency components based on a time-domain time
difference analysis or a frequency-domain group delay analysis,
only of the fundamental frequency components.
2. The computer-implemented method as claimed in claim 1, wherein
the first and second periodic physiological signals comprise
different signal types or the same signal type.
3. The computer-implemented method as claimed in claim 1, wherein
the first and second periodic physiological signals comprise one or
more of: a PPG sensor signal; or an ECG signal; or a pressure
sensor signal; or an acceleration sensor signal.
4. The computer-implemented method as claimed in claim 1,
comprising receiving the first and second periodic physiological
signals from different body locations.
5. The computer-implemented method as claimed in claim 1,
comprising receiving the first and second periodic physiological
signals from the same body location.
6. The computer-implemented method as claimed in claim 1,
comprising extracting the identified fundamental frequency
component.
7. The computer-implemented method as claimed in claim 6, wherein:
extracting a fundamental frequency comprises applying a band pass
filter in the frequency-domain; and determining the time difference
comprises aligning the band pass filtered components in the
time-domain and determining a time shift needed for the
alignment.
8. The computer-implemented method as claimed in claim 7, wherein
aligning the fundamental frequency components comprises maximizing
a cross correlation between the fundamental frequency
components.
9. The computer-implemented method as claimed in claim 1, wherein
determining the time difference comprises obtaining a frequency
spectrum for the first and second periodic physiological signals;
and: determining a group delay time difference from the phase in
the frequency spectrum in the vicinity of the fundamental
frequency; or combining the frequency spectra for the first and
second periodic physiological signals and determining a group delay
of the combined frequency spectrum.
10. The computer-implemented method as claimed in claim 1, wherein
the two related physiological signals allow for determination of a
pulse transit time, and the determination of the pulse transit time
allow for determination of a blood pressure.
11. A non-transitory computer readable medium storing instructions
that, when executed by one or more processors, causes the one or
more processors to: identify a fundamental frequency component of a
first periodic physiological signal and a second periodic
physiological signal, wherein the second periodic physiological
signal is induced by the same physiological process as the first
periodic physiological signal; and determine a time difference
between the fundamental frequency components based on a time-domain
time difference analysis or a frequency-domain group delay
analysis, only of the fundamental frequency components.
12. A system for analyzing two related physiological signals to
determine a time difference between them, comprising: an input for
receiving a first periodic physiological signal and a second
periodic physiological signal induced by the same physiological
process as the first periodic physiological signal; and a
controller, which is adapted to: identify a fundamental frequency
component of the first and second signals; and determine a time
difference between the fundamental frequency components based on a
time-domain time difference analysis or a frequency-domain group
delay analysis, only of the fundamental frequency components.
13. The system as claimed in claim 12, further comprising first and
second physiological sensors, wherein: the first and second
physiological sensors comprise one or more of a PPG sensor, an ECG
sensor, a pressure sensor, or an acceleration sensor; or the first
and second physiological sensors comprise different sensors adapted
to detect different signal types.
14. The system as claimed in claim 12, wherein the controller
comprises: a band pass filter for extracting the identified
fundamental frequency component; and a cross correlator for
aligning the fundamental frequency components thereby to find a
time difference for which a maximized cross correlation is
found.
15. The system as claimed in claim 12, wherein the controller
comprises: a frequency analyzer for analyzing the first and second
periodic physiological signals; and group delay unit for finding a
group delay from the phase in the frequency spectrum in the
vicinity of the fundamental frequency and for determining a time
difference between the group delays.
Description
FIELD OF THE INVENTION
[0001] This invention relates to the processing of physiological
signals, for example for blood pressure monitoring.
BACKGROUND OF THE INVENTION
[0002] Most, if not all, physiological processes are periodic in
nature, where the period itself exhibits some (non-linear)
variation. Examples include heart rate, breathing rate and walking
gait. These phenomena can be measured using sensors. Depending on
the used sensing principle the sensor signals themselves may have
different shapes, but the periodicity will remain.
[0003] For example, a beating heart generates a periodic electrical
signal, normally detected with an ECG, a periodic acoustic signal,
normally detected with a stethoscope at the chest, and a periodic
pressure wave, normally detected with a stethoscope at the limbs to
evaluate blood pressure. Walking generates many periodic
acceleration signals, which differ depending on body locations,
which differences depend also on the rigidity of the body. There
are also periodic acoustic signals due to the interaction with the
ground, periodic pressure signals at the floor when touching the
ground etc.
[0004] There is an increasing demand for unobtrusive health sensing
systems based on monitoring physiological signals. In particular,
there is a shift from conventional hospital treatment towards
unobtrusive vital signs sensor technologies, centered around the
individual, to provide better information about the subject's
general health.
[0005] Such vital signs monitoring systems help to reduce treatment
costs by disease prevention and enhance the quality of life. They
may provide improved physiological data for physicians to analyze
when attempting to diagnose a subject's general health condition.
Vital signs monitoring typically includes monitoring one or more of
the following physical parameters: heart rate, blood pressure,
respiratory rate and core body temperature.
[0006] This invention relates generally to processing of periodic
physiological signals. However, for the purposes of explanation,
one particular application to blood pressure monitoring is
discussed in detail.
[0007] In the US about 30% of the adult population has a high blood
pressure. Only about 52% of this population have their condition
under control. Hypertension is a common health problem which has no
obvious symptoms and may ultimately cause death, and is therefore
often referred to as the "silent killer". Blood pressure generally
rises with aging and the risk of becoming hypertensive in later
life is considerable. About 66% of the people in age group 65-74
have a high blood pressure. Persistent hypertension is one of the
key risk factors for strokes, heart failure and increased
mortality.
[0008] The condition of the hypertensive patients can be improved
by lifestyle changes, healthy dietary choices and medication.
Particularly for high risk patients, continuous 24 hour blood
pressure monitoring is very important and there is obviously a
desire for systems which do not impede ordinary daily life
activities.
[0009] There are two main classes of method to monitor blood
pressure.
[0010] For invasive direct blood pressure monitoring, the gold
standard is by catheterization. A strain gauge in fluid is placed
in direct contact with blood at any artery site. This method is
only used when accurate continuous blood pressure monitoring is
required in dynamic (clinical) circumstances. It is most commonly
used in intensive care medicine and anesthesia to monitor blood
pressure in real time.
[0011] For indirect blood pressure monitoring, oscillometry is a
popular automatic method to measure blood pressure. The method uses
a cuff with integrated pressure sensor. However, these methods are
not suitable for performance by the user.
[0012] More non-invasive methods to estimate blood pressure are
based on pulse wave velocity (PWV). This technique is based on the
fact that the velocity of the pressure pulse traveling through an
artery is related to blood pressure. The PWV is derived from the
pulse transit time (PTT). Usually the pulse transit time is
estimated by: (i) sampling and filtering the waveforms, (ii)
detecting beats in the waveforms, (iii) detecting features within
the beats that serve as a reference point (pulse arrival moment)
and (iv) calculating the PTT as the time delay between the
features. Thus, measurement of PTT involves determining a time
difference between two physiological signals.
[0013] The PWV is then obtained by: PWV=D/PTT, where D refers to
the pulse travel distance. The PWV can be translated to a blood
pressure estimate by means of a calibration step. Often the
Moens-Korteweg equation is applied which describes the relationship
between blood pressure and PWV. If the pulse travel distance D is
stable during a measurement, the blood pressure can be directly
inferred from PTT by means of a calibration function.
[0014] The PTT is measurement is one example where the time shift
between different signals generated by the same event can provide
important information. The method currently used to determine the
time shift between different signals generated by the same
phenomenon requires making physiological assumptions about the
shape of the wave to detect identifiable features as mentioned
above. This can be very sensitive to signal noise.
[0015] The determination of the two time instants becomes even more
cumbersome when the two signal shapes are different. It is hard to
identify which part of either curve corresponds to the same
phase.
[0016] WO 2017/147609 discloses a system for calculating the PTT by
processing a pair of PPG or ECG signals. Time-domain shift methods
and frequency-domain phase measurement methods are disclosed.
[0017] There remains a need for a method and system which can
determine time differences between related periodic signals (in
particular signals having the same frequency) in a more reliable
and automated manner.
SUMMARY OF THE INVENTION
[0018] The invention is defined by the claims.
[0019] According to examples in accordance with an aspect of the
invention, there is provided a computer-implemented method of
analyzing two related physiological signals to determine a time
difference between them, comprising:
[0020] receiving a first periodic physiological signal;
[0021] receiving a second periodic physiological signal induced by
the same physiological process as the first periodic physiological
signal;
[0022] identifying a fundamental frequency component of the first
and second signals; and
[0023] determining a time difference between the fundamental
frequency components based on a time-domain time difference
analysis or a frequency-domain group delay analysis, only of the
fundamental frequency components.
[0024] This method enables automated detection of a time shift
between two signals, based on identification and analysis of their
fundamental frequency components and their associated phase
relationship. The physiological signals are signals generated by
the same phenomenon with a single characteristic frequency (at a
given point in time) and hence the same period as each other. The
method is robust against varying and different wave-shapes, in this
way providing a more stable and accurate determination of the time
difference.
[0025] The fundamental frequency is for example obtained from a
spectral analysis such as a Fourier Transform applied to each
signal, and identification of the peak in the transform.
[0026] The signal processing maintains the phase of the fundamental
frequency component so that relative timing between the two
fundamental frequency components is preserved. The time difference
is determined based on analysis only of the fundamental frequency
components (for both the frequency-domain approach and for the
time-domain approach). By this is meant that the fundamental
frequency components are extracted for analysis or else the signal
processing takes place only at the frequency of the fundamental
frequency component. In this way, timing is obtained based on
signals which have corresponding time-domain characteristics
(shape) or frequency-domain characteristics, so that reliable time
delay calculation becomes possible.
[0027] The signal processing implements an effective normalization
of the two signals based on their fundamental frequency
components.
[0028] The first and second periodic physiological signals comprise
different signal types or the same signal type.
[0029] The first and second periodic physiological signals may
comprise one or more of:
[0030] a PPG sensor signal; or
[0031] an ECG signal; or
[0032] a pressure sensor signal; or
[0033] an acceleration sensor signal.
[0034] The signals may thus be of the same type and hence with the
same basic frequency characteristics, but a time difference between
the signals is of interest. This is for example possible when the
first and second periodic physiological signals are received from
different body locations.
[0035] In this case, a time delay between signals which have
different shape may be of interest. This is for example possible
when the first and second periodic physiological signals are
received from the same body location. The different signals may
have a different time response to a stimulus, in particular the
signal shape (wave form) can be different, and the time difference
is again of interest. By using on the fundamental frequency
components for analysis, shape normalization takes place.
[0036] In a first set of examples, the method comprises extracting
the identified fundamental frequency component. In this way, a new
signal is generated which represents the original periodic
physiological signals but which a much more easily identifiable
phase. The extraction maintains the phase of the fundamental
frequency components.
[0037] Extracting the fundamental frequency for example comprises
applying a band pass filter in the frequency-domain.
[0038] The time difference may then be determined by aligning the
band pass filtered components in the time-domain and determining a
time shift needed for the alignment. Aligning the fundamental
frequency components for example comprises maximizing a cross
correlation between the fundamental frequency components.
[0039] By extracting the fundamental frequency component, for
example with a band pass filter, the signals are processed such
that they again have similar (sinusoidal) shapes. By having similar
shapes the (cross) correlation will be larger at the correct time
lag, while being low at other relative timing values.
[0040] This provides a time-domain approach by which the processing
of the signals based on different candidate time delays results in
the actual time difference between the fundamental frequency
components being found.
[0041] In a second set of examples, determining the time difference
comprises: [0042] obtaining a frequency spectrum for the first and
second periodic physiological signals; and:
[0043] determining a group delay time difference from the phase in
the frequency spectrum in the vicinity of the fundamental
frequency; or
[0044] combining the frequency spectra for the first and second
periodic physiological signals and determining a group delay of the
combined frequency spectrum.
[0045] This provides a frequency based analysis. The group delay is
indicative of the rate of change of phase with respect to
frequency. At the fundamental frequency, the group delay difference
represents a time lag.
[0046] The method may be used for determining the pulse transit
time, and for determining a blood pressure from the pulse transit
time. In this way, the method enables non-invasive and indirect
measurement of blood pressure by accurately and automatically
determining the pulse transit time between two signals.
[0047] The method may be implemented at least in part in
software.
[0048] The invention also provides a system for analyzing two
related physiological signals to determine a time difference
between them, comprising:
[0049] an input for receiving a first periodic physiological signal
and a second periodic physiological signal induced by the same
physiological process as the first periodic physiological
signal;
[0050] a controller, which is adapted to: [0051] identify a
fundamental frequency component of the first and second signals;
and [0052] determine a time difference between the fundamental
frequency components based on a time-domain time difference
analysis or a frequency-domain group delay analysis, only of the
fundamental frequency components.
[0053] Identifying the fundamental frequency component for example
involves finding the largest component for example based on the
magnitude of the FFT spectrum. There are other options, such as the
use of the discrete cosine transform.
[0054] The fundamental may instead be identified in the
time-domain, by using a peak detector, determining the time between
peaks, and determining the mean (or median). The inverse of the
peak period time corresponds to the fundamental frequency.
[0055] The system may further comprise first and second
physiological sensors, wherein:
[0056] the first and second physiological sensors both comprise a
PPG sensor, an ECG sensor, a pressure sensor or an acceleration
sensor; or
[0057] the first and second periodic physiological signals comprise
different sensor from the set of a PPG sensor, an ECG sensor, a
pressure sensor and an acceleration sensor.
[0058] In one set of examples, the controller comprises:
[0059] a band pass filter for extracting the identified fundamental
frequency component; and
[0060] a cross correlator for aligning the fundamental frequency
components thereby to find a time difference for which a maximized
cross correlation is found.
[0061] This provides a time-domain analysis.
[0062] In another set of examples, the controller comprises:
[0063] a frequency analyzer for analyzing the first and second
periodic physiological signals; and
[0064] group delay unit for finding a group delay from the phase in
the frequency spectrum in the vicinity of the fundamental frequency
and for determining a time difference between the group delays.
[0065] This provides a frequency-domain analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
[0066] Examples of the invention will now be described in detail
with reference to the accompanying drawings, in which:
[0067] FIG. 1 shows a schematic example of a system for determining
a time difference between physiological signals;
[0068] FIG. 2 shows the units of a controller for a first
processing approach;
[0069] FIG. 3 shows the processing steps carried out by the
controller of FIG. 2
[0070] FIG. 4 shows the units of a controller for a second
processing approach;
[0071] FIG. 5 shows the processing steps carried out by the
controller of FIG. 4; and
[0072] FIG. 6 shows a method for determining a time difference
between physiological signals.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0073] The invention will be described with reference to the
Figures.
[0074] It should be understood that the detailed description and
specific examples, while indicating exemplary embodiments of the
apparatus, systems and methods, are intended for purposes of
illustration only and are not intended to limit the scope of the
invention. These and other features, aspects, and advantages of the
apparatus, systems and methods of the present invention will become
better understood from the following description, appended claims,
and accompanying drawings. It should be understood that the Figures
are merely schematic and are not drawn to scale. It should also be
understood that the same reference numerals are used throughout the
Figures to indicate the same or similar parts.
[0075] The invention provides a method and system for analyzing two
related physiological signals to determine a time difference
between them. The fundamental frequency components are analyzed to
determine a time difference. This approach is robust against
varying and different wave shapes, providing a stable and accurate
determination of a time difference.
[0076] FIG. 1 shows the system 10 in simplified schematic form. It
comprises an input 12 for receiving a first periodic physiological
signal 14 and a second periodic physiological signal 16 having the
same period as the first periodic physiological signal. The first
periodic signal is generated by a first physiological sensor 18 and
the second periodic signal 16 is generated by a second
physiological sensor 20.
[0077] The first and second physiological sensors may each comprise
a PPG sensor, an ECG sensor, a pressure sensor or an acceleration
sensor. Other sensors are also possible. The two sensors may be of
the same type or they may be of different type. However, in all
cases, the two signals have the same fundamental (dominant)
frequency component, typically because this frequency component is
induced by a common trigger, such as the heartbeat, or the
respiration cycle, or walking. In other words, the two signals may
share a common underlying process, e.g. the physiological process.
The underlying process, identifiable by a process specific signal
component, produces signals which conduct through different
physical (sensing modalities) and physiological paths to reach the
sensors. Thus, the signals detected by the sensors will both
contain the process specific signal component associated with the
common trigger, even when measuring different signal types.
[0078] The common trigger may be associated with any component of a
signal that is shared between the signals measured by the first and
second physiological sensors caused by the common underlying
process. In other words, if the first and second physiological
sensors detect signals containing at least one shared process
specific signal component, the signals may be identified as sharing
a common trigger. This is particularly relevant where the first and
second physiological sensors are different types of sensors.
[0079] The invention is of interest when the relative timing of two
signals, i.e. their phase difference, conveys useful information.
One example is for the determination of pulse transit time, where
the arrival of signals (having timing originally linked to the
heartbeat) at different parts of the body enables physiological
information to be derived. There are however other examples. For
example, different signals may be measured at the same point, and
time differences between those signals may provide information of
relevance.
[0080] The system comprises a controller 22, which has a Fourier
Transform unit 24 for identifying a fundamental frequency component
of the first and second signals and a time calculation unit 26
which determines a time difference between the fundamental
frequency components.
[0081] Identifying the fundamental frequency component for example
involves finding the largest magnitude component of the FFT
spectrum. However, there are other options, such as the use of the
discrete cosine transform.
[0082] The fundamental frequency may instead be identified in the
time-domain, by using a peak detector, determining the time between
peaks, and determining the mean (or median). The inverse of the
peak period time corresponds to the fundamental frequency. Since
both signals have the same fundamental frequency, the analysis of
both signals may be used to refine the estimate of the fundamental
frequency. Instead of using peak detection, another characteristic
such as like valley detection or zero-crossing may be used.
[0083] There are different ways to determine the time
difference.
[0084] FIG. 2 shows a first example of an implementation based on a
time-domain analysis. The controller 22 comprises the Fourier
Transform unit 24 (e.g. a Fast Fourier Transform unit), a band pass
filter 30 for extracting the identified fundamental frequency
component and a cross correlator 32 for aligning the fundamental
frequency components thereby to find a time difference for which a
maximized cross correlation is found.
[0085] The band pass filter is centered at the fundamental
frequency to remove noise and signal specific wavelengths, but it
keeps the periodicity related to the underlying phenomenon. The
resulting two near-sinusoidal curves are aligned by maximizing the
cross correlation of the two filtered signals for varying delay.
The time difference is the smallest delay (lag) at which the cross
correlation maximizes.
[0086] FIG. 3 shows this processing approach graphically.
[0087] The first physiological signal is shown as pane 40 and the
second physiological signal is shown as pane 42. They are both
processed by the band pass filter 30 to result in quasi-sinusoidal
signals 44, 46.
[0088] The cross correlation signal 48 is then obtained and the
maximum amplitude ("Argmax") corresponds to the time delay
.DELTA.t.
[0089] FIG. 4 shows a second example of an implementation based on
a frequency-domain analysis. The controller 22 comprises the
Fourier Transform unit 24 (e.g. a Fast Fourier Transform unit), and
a group delay unit 50. The Fourier Transform unit 24 functions as a
frequency analyzer for analyzing the first and second periodic
physiological signals. The group delay unit is for finding a group
delay from the phase in the frequency spectrum in the vicinity of
the fundamental frequency. The time delay is derived from the
difference between the group delays.
[0090] The group delay, is the (negative) derivative of phase (lag)
by frequency.
[0091] For example, it is assumed that a sinusoidal signal
y=sin(.omega.t+p) is delayed to
sin(.omega.(t-d)+p)=sin(.omega.t+(p-.omega.d)).
[0092] The phase lag is equal to -.omega.d and the group delay (the
differential with respect to w) is equal to -d (compared to zero
for the non-delayed signal). Thus, the difference in group delay
corresponds to the time difference.
[0093] FIG. 5 shows this processing approach graphically.
[0094] The first physiological signal is again shown as pane 40 and
the second physiological signal is shown as pane 42. They are both
converted to frequency spectra to result in the signals 52, 54.
[0095] The phase is analyzed for the frequency centered at the
identified fundamental frequency as represented by panes 56,
58.
[0096] The corresponding group delays GD1 60 and GD2 62 are
obtained and the difference corresponds to the time delay.
[0097] Note that the group delay calculation is essentially a
differentiation. The differentiation and the time difference
calculation may be carried out in either order. Thus, this approach
generally involves determining a group delay time difference from
the phase in the frequency spectrum in the vicinity of the
fundamental frequency. It may involve a phase difference
calculation followed by a group delay calculation (the phase
spectrum, after differentiation by frequency, becomes a group delay
time value) or a group delay calculation followed by a time
difference calculation (as explained fully above).
[0098] As an alternative, the two FFT signals can be first
multiplied in the complex domain, and the group delay of the
product is determined. This group delay is again the time
difference of interest. Thus, in this case the frequency spectra
for the first and second periodic physiological signals are first
combined (multiplied) and the group delay of the combined frequency
spectrum is then obtained.
[0099] In the complex frequency domain, the components may be
expressed as A exp(p), where A is the magnitude and p the phase
(both dependent on frequency). Multiplying (with one complex
conjugate) leads to subtracting phases:
[A.sub.0 exp(p.sub.0)][A.sub.1exp(p.sub.1)]*=A.sub.0
exp(p.sub.0)A.sub.1
exp(-p.sub.1)=A.sub.0A.sub.1exp(p.sub.0-p.sub.1)
[0100] FIG. 6 shows a method of analyzing two related physiological
signals to determine a time difference between them,
comprising:
[0101] in step 70, receiving a first periodic physiological
signal;
[0102] in step 72, receiving a second periodic physiological signal
having the same period as the first periodic physiological signal
(because they are induced by the same physiological process);
[0103] in step 74 identifying a fundamental frequency component of
the first and second signals; and
[0104] in step 76 determining a time difference between the
fundamental frequency components.
[0105] This method enables automated detection of a time shift
between two signals, based on identification and analysis of their
fundamental frequency components and their associated phase
relationship.
[0106] As mentioned above, one area of interest for the invention
is in the monitoring of blood pressure. Specifically, signals
related to heart rate can be recorded and compared with the system
above. These signals may be of any type, such as PPG signals, ECG
signals or pressure sensor signals. The pulse transit time is
output as the time difference, and this can be used to monitor
variations in blood pressure. A relative decrease in pulse transit
time is associated with an increase in blood pressure. This is due
to increased wave velocity due to the higher blood pressure. When
the measuring locations are given, and thus the distance between
measuring locations is known, the exact wave velocity can be
calculated and thus an estimation of the absolute blood pressure is
also possible.
[0107] The invention may also be used to define mechanical
compliance of a system. For example, the system may be used to
measure the rigidity of the human body during walking.
Specifically, when the method is used to compare signals from
different body locations (for example the neck and hip), the
resulting time lag will be a function of the rigidity of the part
of the system between the locations (in the example, the rigidity
of the trunk). It is known that increased body rigidity is
associated with increased fall risk so the system may be used as a
monitoring system for vulnerable subjects. Together with fall risk
monitoring, body rigidity could be used by a home monitoring system
to monitor people with specific diseases related to posture. For
example, people with Parkinson's disease show increased posture
rigidity with the progression of the disease.
[0108] The time shift between two different types of signal
measured at the same site can also give information about the
originating event. For example, the delay between an ECG signal and
the mechanical pulse auscultated at the chest indicates the
mechanical answer time of the heart to the electrical stimulus
given by a sinoatrial node. An increased delay indicates a
decreased contractility of the heart tissues, which might lead to
heart failure.
[0109] This method could also be used the opposite way: when the
wearing location of one of the measuring sites is unknown, the time
alignment could give indications about the distance of the other
measuring site, and thus estimate the actual location. This method
could be used to monitor changes in the wearing location. Changes
in the time alignment over time might indicate a shift in the
wearing location (example an arm/chest band that slips, or a
pendant that lays in different positions).
[0110] Another example of a possible application of the invention
is the comparison of breathing signals (for example by
plethysmography) with the periodic variations in heart rate (for
example by an optical heart rate monitor). The resulting delay can
give an indication of how quickly the nervous system is capable of
adapting heart rate to the internal pressure induced by breathing,
and therefore give an estimation of the responsiveness of the
nervous system.
[0111] As discussed above, a controller performs the data
processing. The controller can be implemented in numerous ways,
with software and/or hardware, to perform the various functions
required. A processor is one example of a controller which employs
one or more microprocessors that may be programmed using software
(e.g., microcode) to perform the required functions. A controller
may however be implemented with or without employing a processor,
and also may be implemented as a combination of dedicated hardware
to perform some functions and a processor (e.g., one or more
programmed microprocessors and associated circuitry) to perform
other functions.
[0112] Examples of controller components that may be employed in
various embodiments of the present disclosure include, but are not
limited to, conventional microprocessors, application specific
integrated circuits (ASICs), and field-programmable gate arrays
(FPGAs).
[0113] In various implementations, a processor or controller may be
associated with one or more storage media such as volatile and
non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM.
The storage media may be encoded with one or more programs that,
when executed on one or more processors and/or controllers, perform
the required functions. Various storage media may be fixed within a
processor or controller or may be transportable, such that the one
or more programs stored thereon can be loaded into a processor or
controller.
[0114] Other variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed invention, from a study of the drawings, the
disclosure, and the appended claims. In the claims, the word
"comprising" does not exclude other elements or steps, and the
indefinite article "a" or "an" does not exclude a plurality. The
mere fact that certain measures are recited in mutually different
dependent claims does not indicate that a combination of these
measures cannot be used to advantage. Any reference signs in the
claims should not be construed as limiting the scope.
* * * * *